from google.adk.agents import LlmAgent

agent = LlmAgent(
    name="creative_writing_agent",
    model="gemini-3-flash-preview",
    description="A creative writing assistant",
    instruction="""
    You are a creative writing assistant.
    Help users develop story ideas and characters.
    """
)

from google.adk.agents import LlmAgent

openai_agent = LlmAgent(
    name="openai_agent",
    model="gpt-4o",
    description="OpenAI-powered agent"
)

claude_agent = LlmAgent(
    name="claude_agent",
    model="claude-3-5-sonnet-20241022",
    description="Claude-powered agent"
)

from pydantic import BaseModel
from google.adk.agents import LlmAgent

class SupportTicket(BaseModel):
    customer_name: str
    issue_type: str
    priority: str
    description: str

agent = LlmAgent(
    name="ticket_agent",
    model="gemini-3-flash-preview",
    output_schema=SupportTicket
)

from google.adk.agents import LlmAgent
from google.adk.tools import GoogleSearchTool, CodeExecutionTool

agent = LlmAgent(
    name="researcher",
    model="gemini-3-flash-preview",
    tools=[GoogleSearchTool(), CodeExecutionTool()]
)

def calculate_price(quantity: int, unit_price: float) -> float:
    """Calculate total price"""
    return quantity * unit_price

agent = LlmAgent(
    name="calculator",
    tools=[calculate_price]
)

from langchain.tools import WikipediaQueryRun
from google.adk.tools import LangChainTool

wiki_tool = LangChainTool(WikipediaQueryRun())
agent = LlmAgent(tools=[wiki_tool])

from google.adk.mcp import MCPClient

# Connect to MCP server
mcp_client = MCPClient("filesystem")
tools = mcp_client.get_tools()
agent = LlmAgent(tools=tools)

from google.adk.agents import LlmAgent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySession

session = InMemorySession()
runner = Runner(agent, session=session)

# Conversation with memory
result1 = runner.run("My name is Alice")
result2 = runner.run("What's my name?")  # Remembers!

from google.adk.sessions import SQLiteSession

session = SQLiteSession(db_path="conversations.db")
runner = Runner(agent, session=session)

# Conversations persist across restarts

def on_agent_start(context):
    print(f"Agent {context.agent.name} starting")

def on_agent_end(context):
    print(f"Agent completed in {context.duration}s")

agent = LlmAgent(
    name="monitored_agent",
    callbacks=[
        AgentCallback(
            on_start=on_agent_start,
            on_end=on_agent_end
        )
    ]
)

def on_llm_request(context):
    print(f"Sending request: {context.messages}")

def on_llm_response(context):
    print(f"Received: {context.response}")

def on_tool_start(context):
    print(f"Executing tool: {context.tool_name}")

def on_tool_end(context):
    print(f"Tool result: {context.result}")

from google.adk.plugins import Plugin

class LoggingPlugin(Plugin):
    def on_request(self, context):
        log.info(f"Request: {context.input}")
    
    def on_response(self, context):
        log.info(f"Response: {context.output}")
    
    def on_error(self, context):
        log.error(f"Error: {context.error}")

# Apply globally
runner = Runner(agent, plugins=[LoggingPlugin()])

from google.adk.agents import LlmAgent

# Specialized agents
research_agent = LlmAgent(
    name="researcher",
    model="gemini-3-flash-preview",
    instruction="Conduct comprehensive web research",
    tools=[GoogleSearchTool()]
)

summarizer_agent = LlmAgent(
    name="summarizer",
    instruction="Synthesize research into insights"
)

critic_agent = LlmAgent(
    name="critic",
    instruction="Analyze quality and provide recommendations"
)

# Coordinator
coordinator = LlmAgent(
    name="coordinator",
    instruction="Orchestrate research workflow",
    tools=[
        research_agent.as_tool(),
        summarizer_agent.as_tool(),
        critic_agent.as_tool()
    ]
)

from google.adk.agents import SequentialAgent, LlmAgent

sequential = SequentialAgent(
    name="business_planner",
    sub_agents=[
        market_research_agent,  # Step 1
        swot_analysis_agent,    # Step 2
        strategy_agent,         # Step 3
        implementation_agent    # Step 4
    ]
)

from google.adk.agents import LoopAgent

loop = LoopAgent(
    name="tweet_crafter",
    sub_agent=draft_agent,
    max_iterations=5,
    stop_condition=lambda ctx: ctx.result.quality_score > 0.9
)

from google.adk.agents import ParallelAgent

parallel = ParallelAgent(
    name="competitor_analyzer",
    sub_agents=[
        product_analyzer,
        pricing_analyzer,
        market_analyzer,
        tech_analyzer
    ],
    merge_strategy="comprehensive_report"
)

# Get API key from:
# https://aistudio.google.com/

pip install google-adk

from google.adk.agents import LlmAgent

agent = LlmAgent(
    name="my_first_agent",
    model="gemini-3-flash-preview",
    description="My first ADK agent",
    instruction="You are a helpful assistant."
)

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